Value-based purchasing is a quality improvement strategy that links payment with healthcare outcomes. Simply put, we should pay more for better healthcare provided, and less or none for inferior care. Recently, the Centers for Medicare and Medicaid Services (CMS) sought to decrease the rate of hospital-acquired complications by holding hospitals financially accountable for them. Effective October 1, 2008 with the Hospital-Acquired Conditions Initiative, Medicare will not pay hospitals extra when patients develop specific complications. The intended outcome of this policy is simple: with no extra pay to compensate for complication care, hospitals should be motivated to pursue strategies to prevent complications. Yet, the details required for policy implementation are complex, and any change in provider payment has potential for unintended outcomes for patients and providers, the most serious of which would be reduced patient access to healthcare providers. Our objective is to examine how the Hospital-Acquired Conditions Initiative will impact patients, hospitals, and Medicare - focusing on medically-vulnerable patients with increased risks of complications and baseline disparities in healthcare access, and the hospitals who care for these patients. Our Specific aims are: 1) Develop risk-prediction models to predict the risk of specific complications, based upon individual patient- level characteristics and medical conditions; our primary models will focus on the risk of developing decubitus ulcers (bedsores) and urinary tract infections (UTIs) with evaluation of catheter-associated UTIs. 2) Evaluate the impact of non-payment for complications for patients, regarding both intended outcomes (i.e., fewer complications) and unintended outcomes, such as reduced or altered healthcare access for patients at higher risk of complication development, and altered patterns of complication coding that promote payment for complications (such as higher rates of complications coded as present-on-admission, and reduced use of complication-specific diagnosis codes that trigger non-payment). 3) Determine the financial impact of non-payment for these complications for Medicare and hospitals, focusing on resource-limited hospitals (such as safety-net hospitals) and hospitals caring for large numbers of medically-vulnerable patients. Our analysis will use the Healthcare Cost and Utilization Project (HCUP) State Inpatient Database for California, using pre-policy and post-policy discharge data. This database includes the newly required variable to indicate if a condition was present-on-admission or hospital-acquired, and a rich set of variables describing patients, hospitals and payers. Using our risk-prediction model and pre-post analyses, our results will provide critical early feedback to CMS to guide policy revisions and expansions to motivate improved care while avoiding unintended harm to patients at higher risk for complications.